论文标题
使用分散数据收集来解决面部探测器中的偏见和激励措施
Addressing Bias in Face Detectors using Decentralised Data collection with incentives
论文作者
论文摘要
机器学习的最新发展表明，成功的模型不仅依赖大量数据，而且还依赖正确的数据。我们在本文中表明，如何以分散的方式促进这种以数据为中心的方法，以实现算法有效的数据收集。面部探测器是一类模型，由于必须处理大量不同的数据，因此遭受了偏见问题的严重影响。我们还提出了一种面部检测和匿名方法，使用带有面部嵌入的混合多任务级联的CNN来基准多个数据集，以描述和评估模型中对不同种族，性别和年龄段的偏见，以及通过使用模型的数据标记，校正和验证用户的不良系统来丰富公平的方式，并为用户验证，并验证了一个模型。
Recent developments in machine learning have shown that successful models do not rely only on huge amounts of data but the right kind of data. We show in this paper how this data-centric approach can be facilitated in a decentralized manner to enable efficient data collection for algorithms. Face detectors are a class of models that suffer heavily from bias issues as they have to work on a large variety of different data. We also propose a face detection and anonymization approach using a hybrid MultiTask Cascaded CNN with FaceNet Embeddings to benchmark multiple datasets to describe and evaluate the bias in the models towards different ethnicities, gender, and age groups along with ways to enrich fairness in a decentralized system of data labeling, correction, and verification by users to create a robust pipeline for model retraining.
